Application of Bayesian Statistics in Hydraulic Flow Units Modeling and Permeability Prediction (A case study Carbonate Reservoir in SW Iran)

Abstract

The determination of rock types for petrophysical studies has a wide range of applications. It is widely used in drilling, production, and especially in the study and characterization of reservoirs. Zoning of flow units and permeability estimation is one of the challenging tasks of reservoir studies, which uses the integration of data from well logs and analysis of the core. In this study, a Bayesian theory-based statistical modeling method is proposed to identify hydraulic flow units in coreless wells using the concept of hydraulic flow unit and then permeability estimation. In the flow zone indicator (FZI) method, the formation is divided into five hydraulic flow units. In the Winland R35 ethod, however, it is divided into four hydraulic flow units. The Bayesian statistical model divides the existing complex carbonate reservoir rock data into three hydraulic flow units with the most probability of similarity. The second and third hydraulic flow units have closer properties compared to the first hydraulic unit. The Bayesian method-based permeability estimation modeling has acceptable precision, and validation of its results with core data indicates a precision factor of 0.96. The findings of this study can help in better understanding of the concept of flow units and more effective estimation of the permeability of the rocks of the heterogeneous carbonate reservoir.